{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "139002ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penri\\AppData\\Local\\Temp\\ipykernel_24324\\2979793285.py:9: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats('retina')\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Day</th>\n",
       "      <th>Group</th>\n",
       "      <th>Originality</th>\n",
       "      <th>Interestingness</th>\n",
       "      <th>Writing</th>\n",
       "      <th>Coherence</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Evaluator</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>external</th>\n",
       "      <th>Humanlikeness</th>\n",
       "      <th>English</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Ability</th>\n",
       "      <th>DAT</th>\n",
       "      <th>Total</th>\n",
       "      <th>Idea</th>\n",
       "      <th>Outline</th>\n",
       "      <th>Write</th>\n",
       "      <th>Edit</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Again</th>\n",
       "      <th>AI_Helpfulness</th>\n",
       "      <th>AI_Satisfaction</th>\n",
       "      <th>AI_Contribution</th>\n",
       "      <th>DAT_Group</th>\n",
       "      <th>Group_</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Benjamin Joers</td>\n",
       "      <td>1_1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>86.81</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Rio Dharma</td>\n",
       "      <td>1_1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>allison liegner</td>\n",
       "      <td>1_2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Ryan Ho</td>\n",
       "      <td>1_2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Confirmation</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nathan Kidambi</td>\n",
       "      <td>2_1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>Pema Euden</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87.08</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>6.5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>295</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Creativity</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Alan Wu</td>\n",
       "      <td>295_1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>74.63</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>295</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
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       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>295</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Ziqi Yang</td>\n",
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       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Day               Group  Originality  Interestingness  Writing  \\\n",
       "0       1    1    Human Creativity            5                3        4   \n",
       "1       1    1    Human Creativity            5                6        4   \n",
       "2       1    2    Human Creativity            2                1        3   \n",
       "3       1    2    Human Creativity            6                7        7   \n",
       "4       2    1  Human Confirmation            6                5        6   \n",
       "...   ...  ...                 ...          ...              ...      ...   \n",
       "2191  294    2             Copilot            6                4        5   \n",
       "2192  295    1    Human Creativity            5                4        4   \n",
       "2193  295    1    Human Creativity            5                5        6   \n",
       "2194  295    2    Human Creativity            5                5        5   \n",
       "2195  295    2    Human Creativity            5                5        7   \n",
       "\n",
       "      Coherence  Overall        Evaluator unique_id  external  Humanlikeness  \\\n",
       "0             7      4.0   Benjamin Joers       1_1         0            4.5   \n",
       "1             5      5.0       Rio Dharma       1_1         0            4.5   \n",
       "2             5      2.0  allison liegner       1_2         0            3.0   \n",
       "3             6      7.0          Ryan Ho       1_2         0            3.0   \n",
       "4             6      6.0   Nathan Kidambi       2_1         0            6.5   \n",
       "...         ...      ...              ...       ...       ...            ...   \n",
       "2191          5      5.0       Pema Euden     294_2         1            3.0   \n",
       "2192          4      4.0       Pema Euden     295_1         1            6.5   \n",
       "2193          5      5.0          Alan Wu     295_1         1            6.5   \n",
       "2194          6      5.0       Pema Euden     295_2         1            3.5   \n",
       "2195          7      6.0        Ziqi Yang     295_2         1            3.5   \n",
       "\n",
       "      English  Experience  Ability    DAT  Total  Idea  Outline  Write  Edit  \\\n",
       "0         3.0         1.0      2.0  86.81   80.0  12.0      8.0   55.0   5.0   \n",
       "1         3.0         1.0      2.0  86.81   80.0  12.0      8.0   55.0   5.0   \n",
       "2         3.0         1.0      2.0  86.81   85.0  10.0     20.0   15.0  40.0   \n",
       "3         3.0         1.0      2.0  86.81   85.0  10.0     20.0   15.0  40.0   \n",
       "4         2.0         1.0      1.0  89.58    NaN  45.0      NaN    NaN   NaN   \n",
       "...       ...         ...      ...    ...    ...   ...      ...    ...   ...   \n",
       "2191      3.0         3.0      2.0  87.08   45.0   3.0      5.0   25.0  12.0   \n",
       "2192      2.0         1.0      1.0  74.63   66.0   1.0      5.0   40.0  20.0   \n",
       "2193      2.0         1.0      1.0  74.63   66.0   1.0      5.0   40.0  20.0   \n",
       "2194      2.0         1.0      1.0  74.63   55.0   5.0     20.0   25.0   5.0   \n",
       "2195      2.0         1.0      1.0  74.63   55.0   5.0     20.0   25.0   5.0   \n",
       "\n",
       "      Satisfaction  Flexibility  Goal  Again  AI_Helpfulness  AI_Satisfaction  \\\n",
       "0              1.0          4.0   2.0    0.0             NaN              NaN   \n",
       "1              1.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2              3.0          3.0   3.0    0.0             4.0              5.0   \n",
       "3              3.0          3.0   3.0    0.0             4.0              5.0   \n",
       "4              2.0          6.0   2.0    0.0             NaN              NaN   \n",
       "...            ...          ...   ...    ...             ...              ...   \n",
       "2191           7.0          6.0   5.0    1.0             7.0              7.0   \n",
       "2192           5.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2193           5.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2194           3.0          4.0   5.0    1.0             6.0              5.0   \n",
       "2195           3.0          4.0   5.0    1.0             6.0              5.0   \n",
       "\n",
       "      AI_Contribution  DAT_Group  Group_  \n",
       "0                 NaN          3       1  \n",
       "1                 NaN          3       1  \n",
       "2                70.0          3       1  \n",
       "3                70.0          3       1  \n",
       "4                 NaN          3       2  \n",
       "...               ...        ...     ...  \n",
       "2191             91.0          3       3  \n",
       "2192              NaN          2       1  \n",
       "2193              NaN          2       1  \n",
       "2194             71.0          2       1  \n",
       "2195             71.0          2       1  \n",
       "\n",
       "[2196 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.transforms as transforms\n",
    "import seaborn as sns\n",
    "from IPython.display import set_matplotlib_formats\n",
    "%matplotlib inline\n",
    "set_matplotlib_formats('retina')\n",
    "from scipy import stats\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import warnings\n",
    "import matplotlib.path as mpath\n",
    "sns.set(rc={\"figure.dpi\":100, 'savefig.dpi':300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)\n",
    "df = pd.read_csv('Replication Data for Designing Human and Generative AI Collaboration.csv').iloc[:,1:]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4798852f",
   "metadata": {},
   "source": [
    "# A. AI Effect Varies Among Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "298c60b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 584
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Perform Regression Analysis with Control Variables\n",
    "model = smf.ols('Overall ~ C(Day) * C(Group) + Ability + Experience + DAT + English', data=df).fit()\n",
    "\n",
    "# 2. Compute Predicted Means and Confidence Intervals\n",
    "mean_ability = df['Ability'].mean()\n",
    "mean_experience = df['Experience'].mean()\n",
    "mean_DAT = df['DAT'].mean()\n",
    "mean_english = df['English'].mean()\n",
    "\n",
    "# Create a new DataFrame for unique combinations of Day and Group\n",
    "unique_combinations = df[['Day', 'Group']].drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "# Add control variables with their mean values\n",
    "unique_combinations['Ability'] = mean_ability\n",
    "unique_combinations['Experience'] = mean_experience\n",
    "unique_combinations['DAT'] = mean_DAT\n",
    "unique_combinations['English'] = mean_english\n",
    "\n",
    "# Get predictions and confidence intervals\n",
    "predictions = model.get_prediction(unique_combinations)\n",
    "pred_summary = predictions.summary_frame(alpha=0.05)  # 95% CI\n",
    "\n",
    "# Combine the predictions with the unique combinations\n",
    "plot_data = unique_combinations.copy()\n",
    "plot_data['mean'] = pred_summary['mean']\n",
    "plot_data['lower'] = pred_summary['mean_ci_lower']\n",
    "plot_data['upper'] = pred_summary['mean_ci_upper']\n",
    "plot_data['error_lower'] = plot_data['mean'] - plot_data['lower']\n",
    "plot_data['error_upper'] = plot_data['upper'] - plot_data['mean']\n",
    "\n",
    "# 3. Prepare Data for Plotting\n",
    "day_order = sorted(plot_data['Day'].unique())\n",
    "group_order = ['Human Confirmation', 'Human Creativity', 'Copilot']\n",
    "\n",
    "day_indices = {day: idx for idx, day in enumerate(day_order)}\n",
    "group_indices = {group: idx for idx, group in enumerate(group_order)}\n",
    "\n",
    "bar_width = 0.08  # Adjust as desired to control the dodge effect\n",
    "positions = []\n",
    "labels = []\n",
    "\n",
    "for day in day_order:\n",
    "    for group in group_order:\n",
    "        offset = (group_indices[group] - (len(group_order) - 1) / 2) * bar_width\n",
    "        positions.append(day_indices[day] + offset)\n",
    "        labels.append((day, group))\n",
    "\n",
    "means = []\n",
    "error_lowers = []\n",
    "error_uppers = []\n",
    "\n",
    "for day, group in labels:\n",
    "    row = plot_data[(plot_data['Day'] == day) & (plot_data['Group'] == group)]\n",
    "    if not row.empty:\n",
    "        means.append(row['mean'].values[0])\n",
    "        error_lowers.append(row['error_lower'].values[0])\n",
    "        error_uppers.append(row['error_upper'].values[0])\n",
    "    else:\n",
    "        means.append(np.nan)\n",
    "        error_lowers.append(0)\n",
    "        error_uppers.append(0)\n",
    "\n",
    "# 4. Plot the Results Using Matplotlib\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "markers = ['o', 'x', '^']  # Circle, square, triangle\n",
    "linestyles = ['solid', 'dashed', 'dotted']\n",
    "\n",
    "# Store line objects for the legend (without error bars)\n",
    "line_objects = []\n",
    "\n",
    "for idx, group in enumerate(group_order):\n",
    "    group_positions = [positions[i] for i in range(len(positions)) if labels[i][1] == group]\n",
    "    group_means = [means[i] for i in range(len(means)) if labels[i][1] == group]\n",
    "    group_errors = [[error_lowers[i], error_uppers[i]] for i in range(len(means)) if labels[i][1] == group]\n",
    "    group_errors = np.array(group_errors).T  # Transpose to match errorbar input\n",
    "\n",
    "    # Plot the error bars without markers or lines\n",
    "    ax.errorbar(\n",
    "        group_positions,\n",
    "        group_means,\n",
    "        yerr=group_errors,\n",
    "        fmt='none',          # No markers or line\n",
    "        ecolor='black',\n",
    "        capsize=5,\n",
    "        elinewidth=1\n",
    "    )\n",
    "\n",
    "    # Plot the lines and markers, and store line objects for the legend\n",
    "    line, = ax.plot(\n",
    "        group_positions,\n",
    "        group_means,\n",
    "        marker=markers[idx],\n",
    "        linestyle=linestyles[idx],\n",
    "        color='black',\n",
    "        markersize=8,\n",
    "        alpha=0.8,\n",
    "        label=group\n",
    "    )\n",
    "    line_objects.append(line)\n",
    "\n",
    "# Customize the plot\n",
    "ax.set_xticks([day_indices[day] for day in day_order])\n",
    "ax.set_xticklabels(\n",
    "    ['Day 1 (Writing without AI)' if day == 1 else 'Day 2 (Writing with AI)' for day in day_order],\n",
    "    rotation=0, fontsize=14\n",
    ")\n",
    "\n",
    "# Adjust x-axis limits to add padding\n",
    "x_min = min(day_indices.values()) - 0.1\n",
    "x_max = max(day_indices.values()) + 0.1\n",
    "ax.set_xlim(x_min, x_max)\n",
    "\n",
    "ax.set_xlabel('  ', fontsize=14, labelpad=5)\n",
    "ax.set_ylabel('Overall Quality', fontsize=14, labelpad=5)\n",
    "ax.set_title('', fontsize=16)\n",
    "\n",
    "# Add the custom legend with only line objects\n",
    "ax.legend(handles=line_objects, frameon=False, labels=group_order, title='', loc='lower left')\n",
    "\n",
    "ax.set_ylim(4.2, 5.5)\n",
    "ax.set_xlim(-0.5, 1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "ax.figure.savefig(\"Fig 2A - AI Effect Varies Among Groups.pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d56274ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Overall</td>     <th>  R-squared:         </th> <td>   0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Nov 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:44:29</td>     <th>  Log-Likelihood:    </th> <td> -1850.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1096</td>      <th>  AIC:               </th> <td>   3715.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1089</td>      <th>  BIC:               </th> <td>   3750.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                     <td></td>                                        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                               <td>    3.5160</td> <td>    0.499</td> <td>    7.047</td> <td> 0.000</td> <td>    2.537</td> <td>    4.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Group, Treatment(reference='Human Confirmation'))[T.Copilot]</th>          <td>   -0.1443</td> <td>    0.099</td> <td>   -1.463</td> <td> 0.144</td> <td>   -0.338</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Group, Treatment(reference='Human Confirmation'))[T.Human Creativity]</th> <td>    0.0061</td> <td>    0.098</td> <td>    0.062</td> <td> 0.951</td> <td>   -0.187</td> <td>    0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ability</th>                                                                 <td>    0.1140</td> <td>    0.070</td> <td>    1.634</td> <td> 0.103</td> <td>   -0.023</td> <td>    0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Experience</th>                                                              <td>   -0.1594</td> <td>    0.066</td> <td>   -2.423</td> <td> 0.016</td> <td>   -0.288</td> <td>   -0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DAT</th>                                                                     <td>    0.0146</td> <td>    0.006</td> <td>    2.550</td> <td> 0.011</td> <td>    0.003</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>English</th>                                                                 <td>    0.0866</td> <td>    0.097</td> <td>    0.895</td> <td> 0.371</td> <td>   -0.103</td> <td>    0.276</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>54.482</td> <th>  Durbin-Watson:     </th> <td>   1.856</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  61.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.582</td> <th>  Prob(JB):          </th> <td>3.54e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.003</td> <th>  Cond. No.          </th> <td>1.04e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.04e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Overall   R-squared:                       0.016\n",
       "Model:                            OLS   Adj. R-squared:                  0.011\n",
       "Method:                 Least Squares   F-statistic:                     2.974\n",
       "Date:                Thu, 07 Nov 2024   Prob (F-statistic):            0.00693\n",
       "Time:                        22:44:29   Log-Likelihood:                -1850.3\n",
       "No. Observations:                1096   AIC:                             3715.\n",
       "Df Residuals:                    1089   BIC:                             3750.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================================================================\n",
       "                                                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                   3.5160      0.499      7.047      0.000       2.537       4.495\n",
       "C(Group, Treatment(reference='Human Confirmation'))[T.Copilot]             -0.1443      0.099     -1.463      0.144      -0.338       0.049\n",
       "C(Group, Treatment(reference='Human Confirmation'))[T.Human Creativity]     0.0061      0.098      0.062      0.951      -0.187       0.199\n",
       "Ability                                                                     0.1140      0.070      1.634      0.103      -0.023       0.251\n",
       "Experience                                                                 -0.1594      0.066     -2.423      0.016      -0.288      -0.030\n",
       "DAT                                                                         0.0146      0.006      2.550      0.011       0.003       0.026\n",
       "English                                                                     0.0866      0.097      0.895      0.371      -0.103       0.276\n",
       "==============================================================================\n",
       "Omnibus:                       54.482   Durbin-Watson:                   1.856\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               61.945\n",
       "Skew:                          -0.582   Prob(JB):                     3.54e-14\n",
       "Kurtosis:                       3.003   Cond. No.                     1.04e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.04e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols(\"Overall ~ C(Group, Treatment(reference='Human Confirmation')) + Ability + Experience + DAT + English\", \n",
    "                data=df[df.Day==1]).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dce75275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Overall</td>     <th>  R-squared:         </th> <td>   0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Nov 2024</td> <th>  Prob (F-statistic):</th>  <td>0.0210</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:44:33</td>     <th>  Log-Likelihood:    </th> <td> -1857.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1068</td>      <th>  AIC:               </th> <td>   3729.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1061</td>      <th>  BIC:               </th> <td>   3764.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                     <td></td>                                        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                               <td>    5.0622</td> <td>    0.528</td> <td>    9.583</td> <td> 0.000</td> <td>    4.026</td> <td>    6.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Group, Treatment(reference='Human Confirmation'))[T.Copilot]</th>          <td>    0.3158</td> <td>    0.105</td> <td>    3.006</td> <td> 0.003</td> <td>    0.110</td> <td>    0.522</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Group, Treatment(reference='Human Confirmation'))[T.Human Creativity]</th> <td>    0.3376</td> <td>    0.106</td> <td>    3.196</td> <td> 0.001</td> <td>    0.130</td> <td>    0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ability</th>                                                                 <td>   -0.0205</td> <td>    0.074</td> <td>   -0.276</td> <td> 0.783</td> <td>   -0.166</td> <td>    0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Experience</th>                                                              <td>   -0.0805</td> <td>    0.071</td> <td>   -1.141</td> <td> 0.254</td> <td>   -0.219</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DAT</th>                                                                     <td>    0.0007</td> <td>    0.006</td> <td>    0.109</td> <td> 0.913</td> <td>   -0.011</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>English</th>                                                                 <td>   -0.0831</td> <td>    0.101</td> <td>   -0.821</td> <td> 0.412</td> <td>   -0.282</td> <td>    0.116</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>55.953</td> <th>  Durbin-Watson:     </th> <td>   1.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  64.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.597</td> <th>  Prob(JB):          </th> <td>1.26e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.885</td> <th>  Cond. No.          </th> <td>1.03e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.03e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Overall   R-squared:                       0.014\n",
       "Model:                            OLS   Adj. R-squared:                  0.008\n",
       "Method:                 Least Squares   F-statistic:                     2.498\n",
       "Date:                Thu, 07 Nov 2024   Prob (F-statistic):             0.0210\n",
       "Time:                        22:44:33   Log-Likelihood:                -1857.7\n",
       "No. Observations:                1068   AIC:                             3729.\n",
       "Df Residuals:                    1061   BIC:                             3764.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================================================================\n",
       "                                                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                   5.0622      0.528      9.583      0.000       4.026       6.099\n",
       "C(Group, Treatment(reference='Human Confirmation'))[T.Copilot]              0.3158      0.105      3.006      0.003       0.110       0.522\n",
       "C(Group, Treatment(reference='Human Confirmation'))[T.Human Creativity]     0.3376      0.106      3.196      0.001       0.130       0.545\n",
       "Ability                                                                    -0.0205      0.074     -0.276      0.783      -0.166       0.125\n",
       "Experience                                                                 -0.0805      0.071     -1.141      0.254      -0.219       0.058\n",
       "DAT                                                                         0.0007      0.006      0.109      0.913      -0.011       0.013\n",
       "English                                                                    -0.0831      0.101     -0.821      0.412      -0.282       0.116\n",
       "==============================================================================\n",
       "Omnibus:                       55.953   Durbin-Watson:                   1.950\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.016\n",
       "Skew:                          -0.597   Prob(JB):                     1.26e-14\n",
       "Kurtosis:                       2.885   Cond. No.                     1.03e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.03e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols(\"Overall ~ C(Group, Treatment(reference='Human Confirmation')) + Ability + Experience + DAT + English\", \n",
    "                data=df[df.Day==2]).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a23a888",
   "metadata": {},
   "source": [
    "# B. Quality Inequality Decreases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e760428c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in X for group 'Human Confirmation': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n",
      "Columns in X_plot for group 'Human Confirmation': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n",
      "Columns in X for group 'Human Creativity': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n",
      "Columns in X_plot for group 'Human Creativity': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n",
      "Columns in X for group 'Copilot': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n",
      "Columns in X_plot for group 'Copilot': ['const', 'Overall_x', 'English', 'Experience', 'Ability', 'DAT']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 584
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "t1 = df[df.Day == 1]\n",
    "t2 = df[df.Day == 2]\n",
    "\n",
    "t1_ = t1.groupby(by='ID')['Overall'].mean().reset_index()\n",
    "t1_ = pd.merge(t1_, t1[['ID', 'Group', 'English', 'Experience', 'Ability', 'DAT']], on=['ID']).drop_duplicates(keep='first').reset_index(drop=True)\n",
    "t2_ = t2.groupby(by='ID')['Overall'].mean().reset_index()\n",
    "t2_ = pd.merge(t2_, t2[['ID', 'Group']], on=['ID']).drop_duplicates(keep='first').reset_index(drop=True)\n",
    "tt_ = pd.merge(t1_, t2_, on=['ID', 'Group'], how='left')\n",
    "\n",
    "tt__ = tt_.copy()\n",
    "\n",
    "def round_to_nearest_half(x):\n",
    "    return np.round(x * 2) / 2  # return np.round(x) \n",
    "\n",
    "tt__['Overall_x'] = round_to_nearest_half(tt__['Overall_x'])\n",
    "\n",
    "# 1. Data Aggregation\n",
    "aggregated_data = tt__.groupby(['Overall_x', 'Group']).agg(\n",
    "    mean_y=('Overall_y', 'mean'),\n",
    "    count=('Overall_y', 'size')\n",
    ").reset_index()\n",
    "\n",
    "\n",
    "# 2. Conduct Regression Analysis and Prepare for Plotting\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "# Remove grid\n",
    "ax.grid(False)\n",
    "\n",
    "# Define markers and line styles for each group\n",
    "group_markers = {\n",
    "    \"Human Confirmation\": 'o',            # Circle\n",
    "    \"Human Creativity\": 'x',              # X marker\n",
    "    \"Copilot\": '^'                        # Triangle\n",
    "}\n",
    "\n",
    "group_linestyles = {\n",
    "    \"Human Confirmation\": 'solid',   # '-'\n",
    "    \"Human Creativity\": 'dashed',    # '--'\n",
    "    \"Copilot\": 'dotted'              # ':'\n",
    "}\n",
    "\n",
    "# Define the desired order of groups\n",
    "groups = ['Human Confirmation', 'Human Creativity', 'Copilot']\n",
    "\n",
    "# Map counts to sizes between 40 and 200 for marker sizes\n",
    "count_min = aggregated_data['count'].min()\n",
    "count_max = aggregated_data['count'].max()\n",
    "\n",
    "def map_size(count, count_min, count_max, size_min=40, size_max=200):\n",
    "    if count_max == count_min:\n",
    "        return size_min\n",
    "    else:\n",
    "        return size_min + (count - count_min) * (size_max - size_min) / (count_max - count_min)\n",
    "\n",
    "# Prepare to store regression results for legend\n",
    "legend_handles = []\n",
    "\n",
    "# Loop through each group\n",
    "for idx, group in enumerate(groups):\n",
    "    # Prepare data for plotting\n",
    "    group_data_agg = aggregated_data[aggregated_data['Group'] == group]\n",
    "    x_agg = group_data_agg['Overall_x']\n",
    "    y_agg = group_data_agg['mean_y']\n",
    "    counts = group_data_agg['count']\n",
    "    sizes = counts.apply(lambda c: map_size(c, count_min, count_max))\n",
    "    marker = group_markers.get(group, 'o')\n",
    "\n",
    "    # Set facecolors conditionally: only use 'none' for markers that have a fill (like 'o' or '^')\n",
    "    if marker in ['o', '^']:  # Filled markers like circle and triangle\n",
    "        facecolor = 'none'\n",
    "    else:  # Unfilled markers like 'x'\n",
    "        facecolor = 'black'  # You can change the color if needed\n",
    "\n",
    "    # Plot scatter points\n",
    "    ax.scatter(\n",
    "        x_agg, y_agg, s=sizes, marker=marker, facecolors=facecolor,\n",
    "        edgecolors='black', alpha=0.5, zorder=2\n",
    "    )\n",
    "\n",
    "    # Regression analysis with control variables\n",
    "    required_vars = ['Overall_x', 'Overall_y', 'English', 'Experience', 'Ability', 'DAT']\n",
    "    group_data = tt_[tt_['Group'] == group][required_vars]\n",
    "    # Ensure variables are numeric\n",
    "    group_data = group_data.apply(pd.to_numeric, errors='coerce')\n",
    "    # Drop rows with any NaNs\n",
    "    group_data = group_data.dropna()\n",
    "\n",
    "    # Check if enough data points are available\n",
    "    if len(group_data) < 2:\n",
    "        print(f\"Not enough data points for group '{group}' to perform regression.\")\n",
    "        continue\n",
    "\n",
    "    # Check for zero variance in x\n",
    "    if group_data['Overall_x'].nunique() == 1:\n",
    "        print(f\"Zero variance in x data for group '{group}'. Cannot compute regression.\")\n",
    "        continue\n",
    "\n",
    "    y = group_data['Overall_y']\n",
    "    X = group_data[['Overall_x', 'English', 'Experience', 'Ability', 'DAT']]\n",
    "    X = sm.add_constant(X, has_constant='add')  # Ensures 'const' is added\n",
    "\n",
    "    # Verify that 'const' is in X\n",
    "    print(f\"Columns in X for group '{group}': {X.columns.tolist()}\")\n",
    "\n",
    "    # Perform multiple regression\n",
    "    model = sm.OLS(y, X).fit()\n",
    "    n = len(y)\n",
    "\n",
    "    # Extract coefficient and standard error for 'Overall_x'\n",
    "    coef = model.params['Overall_x']\n",
    "    std_err = model.bse['Overall_x']\n",
    "\n",
    "    # Calculate confidence intervals for the slope\n",
    "    t_value = stats.t.ppf(1 - 0.025, df=n - X.shape[1])  # degrees of freedom = n - p\n",
    "    ci_lower = coef - t_value * std_err\n",
    "    ci_upper = coef + t_value * std_err\n",
    "\n",
    "    # Generate regression line values over x = [1, 7]\n",
    "    x_vals = np.linspace(1, 7, 100)\n",
    "    # Use mean values of control variables\n",
    "    controls_mean = group_data[['English', 'Experience', 'Ability', 'DAT']].mean()\n",
    "    X_plot = pd.DataFrame({\n",
    "        'Overall_x': x_vals,\n",
    "        'English': controls_mean['English'],\n",
    "        'Experience': controls_mean['Experience'],\n",
    "        'Ability': controls_mean['Ability'],\n",
    "        'DAT': controls_mean['DAT']\n",
    "    })\n",
    "    X_plot = sm.add_constant(X_plot, has_constant='add')\n",
    "    \n",
    "    # Ensure that X_plot columns are in the same order as X columns\n",
    "    X_plot = X_plot[X.columns]\n",
    "\n",
    "    # Verify that 'const' is in X_plot\n",
    "    print(f\"Columns in X_plot for group '{group}': {X_plot.columns.tolist()}\")\n",
    "\n",
    "    # Predict y values\n",
    "    y_vals = model.predict(X_plot)\n",
    "\n",
    "    # Line style for the group\n",
    "    linestyle = group_linestyles[group]\n",
    "\n",
    "    # Plot regression line\n",
    "    ax.plot(\n",
    "        x_vals, y_vals, linestyle=linestyle, color='black', zorder=3\n",
    "    )\n",
    "\n",
    "    # Prepare label with adjusted slope and confidence intervals\n",
    "    slope_formatted = f\"{coef:.2f}\"\n",
    "    ci_lower_formatted = f\"{ci_lower:.2f}\"\n",
    "    ci_upper_formatted = f\"{ci_upper:.2f}\"\n",
    "    label = f\"{group} (Slope: {slope_formatted}, 95% CI = [{ci_lower_formatted}, {ci_upper_formatted}])\"\n",
    "\n",
    "    # Create Line2D object for legend, including marker with black face color\n",
    "    line_handle = Line2D(\n",
    "        [0], [0],\n",
    "        color='black',\n",
    "        linestyle=linestyle,\n",
    "        marker=marker,\n",
    "        markerfacecolor='black',   # Filled marker with black color\n",
    "        markeredgecolor='black',\n",
    "        alpha=0.8,\n",
    "        markersize=8,\n",
    "        label=label\n",
    "    )\n",
    "    legend_handles.append(line_handle)\n",
    "\n",
    "# Adjust plot limits\n",
    "ax.set_xlim(1, 7)\n",
    "ax.set_ylim(1, 7)\n",
    "\n",
    "# Move legend to lower left without border\n",
    "ax.legend(handles=legend_handles, frameon=False, loc='lower left')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Overall Quality (Day 1, Without AI)', labelpad=5, fontsize=14)\n",
    "ax.set_ylabel('Overall Quality (Day 2, With AI)', labelpad=5, fontsize=14)\n",
    "\n",
    "# Adjust layout and show plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "ax.figure.savefig(\"Fig 2B - Quality Inequality Decreases.pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032446de",
   "metadata": {},
   "source": [
    "# C. Additional Quality Metrics Among Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb083f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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t3bpVO3bsUEREhKKjo5WQkKC0tDR5enqqVq1aCggIUL9+/dS7d29jyWRL+vTpo4iICK1Zs0Y7d+5UbGysUlNT5efnp6ZNm2rQoEF67LHH5OXlVWybKlWqpE8++UTr1q3T+vXrdfr0aVWqVEnNmjXTkCFDNHjwYGOpYVtxc3PT5MmTNWjQIH311Vfas2ePEhISlJOTI39/f7Vt21ZDhw5Vt27d5ObmZrGOnj17at68eVqxYoXOnDkjNzc3lgEH/j/GOrgC+jHsicyWh8wGlC/GOjg7+jDsjcyWh8wGlB/GOrgC+jHsibyWh7xmW265ubm59m4EAAC4N7Ro0UKS1LFjR61Zs8bOrQEAAIAlZDYAAADHR2YDAABwbOQ111HB3g0AAAAAAAAAAAAAAAAAAMDRMLkOAAAAAAAAAAAAAAAAAIBCmFwHAAAAAAAAAAAAAAAAAEAhTK4DAAAAAAAAAAAAAAAAAKAQJtcBAAAAAAAAAAAAAAAAAFCIW25ubq69GwEAAAAAAAAAAAAAAAAAgCNh5ToAAAAAAAAAAAAAAAAAAAphch0AAAAAAAAAAAAAAAAAAIUwuQ4AAAAAAAAAAAAAAAAAgEI87N0A2E5MTIy+/fZb7du3T6dPn1ZKSooqVqwoX19ftWnTRn369NGjjz6qSpUq2bupJcrKytLnn3+uLVu2KD4+Xrm5uapVq5YmT56s2NhYLV68WJL0+eefq1u3bnZu7d1x7do1ZWVlyc/Pz2T/xo0bNX36dEnSnDlzNHz4cHs0zym40j1SGunp6UpKSlL9+vVN9u/du1djxoyRJD3//POaMmWKTa4XFxen3r17S5IGDRqkd99912K5kydP6je/+Y1NrgkAzsSVxiMymzkyW9m40v1RGuQ1AHAsrjQekdfMkdfKzpXukdIgswGAY3Gl8YjMZo7MVnaudI+UBpkNQHlgcp0LiI+P11tvvaXIyEjl5uaaHMvMzFRaWpri4uIUHh6uDz74QG+88YZ69Ohhp9ZaZ9q0aQoLCzPZd+7cOfn6+io2NtZOrbKPnJwcrV+/XgsWLNA//vEPsxCJkrniPWKt8PBwzZ07V5MmTTILkfaSlJSk+fPna//+/YqMjLR3cwDgrnHF8YjM9isyW9m44v1hLfIaADgOVxyPyGu/Iq+VnSveI9YiswGA43DF8YjM9isyW9m54j1iLTIbgPLC5DonFxUVpYkTJ+rGjRuSpAYNGigoKEgBAQHy9fVVRkaGTp06pbCwMB04cEDx8fH605/+pDfffFPDhg2zc+sti4mJMQKkv7+/pk6dqkaNGunGjRtq3769fvrpJzu38O4KCwvTrFmz7N0Mp+WK94i1Dhw4oEmTJtm7GWb+7//+T1FRUapXr569mwIAd40rjkdkNlNktjvniveHtchrAOA4XHE8Iq+ZIq+VjSveI9YiswGA43DF8YjMZorMVjaueI9Yi8wGoDwxuc6JHT16VOPHj1d6erok6YUXXtCECRPk4WH6sXbq1EkjRozQhg0bNGvWLGVlZen1119XkyZNFBgYaI+mF+vUqVPG9vjx4xUUFGRyfOLEiZo4ceLdbpbdZGdnF3t86NChGjp06F1qjXNx1XvEWiX1nQcffFDHjx+3+XXr169fbL0ltQsAXI2rjkdkNlNktjvjqveHtchrAOAYXHU8Iq+ZIq/dOVe9R6xFZgMAx+Cq4xGZzRSZ7c656j1iLTIbgPJUwd4NwJ3JyMjQ1KlTjcHx5Zdf1qRJk8wGx4JCQkL00ksvSZKysrI0e/Zss6VgHUH+a5KkRo0a2bElcGaufI8AAJyHK49HZDaUlSvfHwAA5+HK4xF5DbbgyvcIAMB5uPJ4RGaDLbjyPQIAjoDJdU7q66+/VmxsrCSpQ4cOevbZZ60676mnnlLDhg0lSceOHdOePXvKrY13Kicnx9h2d3e3Y0vgzFz5HgEAOA9XHo/IbCgrV74/AADOw5XHI/IabMGV7xEAgPNw5fGIzAZbcOV7BAAcgVsu04+dUlBQkLG86Geffabu3btbfe4333yjEydOKDAwUF26dFH16tVNjl+/fl2hoaHasWOHYmJilJqaqurVq6t58+bq27evhg0bpkqVKlmsu0WLFpKkyZMna8KECdq5c6fWrVunw4cPKzk5WT4+PurYsaNGjBihbt26mZw7evRo7du3r8h2z507V0OHDtWiRYu0ePFiSdLnn39uUs+0adP0zTffqGHDhgoPD9fHH3+sdevW6cqVK/L19VXnzp317rvv6ptvvtH06dMlSbt27dJ9992nFStWKDw8XHFxcapYsaKaNGmi4cOHm/y+/L/+9S999dVXio6OVkpKivz9/fXII49o/PjxqlWrVpFtv3jxotavX699+/YpNjZW169fl7u7u7y9vdW6dWs9+uijGjhwoMnTAxs3bjTaaEn+51+w3Jw5czR8+HCL5fft26evv/5aBw8e1OXLl+Xu7i5/f3916dJFTzzxhFq1amXxvPz3293dXf/973+VnJysFStWKDIyUvHx8crNzVXDhg3Vt29fjRkzxqw/2Ut53CMF+3dwcLDefPNNRUVFScpb8veZZ55RcHCwSV2RkZHasmWLDh8+rKSkJHl5ealBgwbq3r27Ro0apdq1axfbluTkZK1fv1579uzRyZMndf36deXm5qp69epq0aKFevXqpZCQEHl5eRnn7N27V2PGjCmyzsjISNWvX9+k3PPPP68pU6ZIyltSOzo6Wl5eXvrPf/6jqlWrFlnX2rVr9frrr0uS5s+fr6CgIMXFxal3796SpEGDBundd9+VVPw9HhwcrJdfflkPP/ywMjMz1bx5c4WFhRX73sycOVNff/21PDw8tHPnzmLvQQCwFzIbmY3MVjTyGnkNABwBeY28Rl4rHpmNzAYAjoDMRmYjsxWPzEZmA1C+il4HFA7r4sWLxuBYuXJlde3atVTnFx7kCoqMjNSMGTOUnJxssj8pKUlRUVGKiorSsmXLtHDhQgUEBBRZT25urmbNmqXQ0FCT/YmJiQoPD1d4eLieeeYZvfLKK6Vqe2n87W9/M7n+pUuXJElubm4m5U6fPq3p06crPj7eZP+hQ4d06NAhHT58WLNnz9brr79u9nri4uK0atUqRUREaP369RYDwYoVK/Tuu+8qMzPT7Fh6erouXryo7du3KzQ0VMuWLVOVKlXu+DVbcvPmTc2YMUPh4eFmx2JjYxUbG6t169Zp5MiRmj59erHLAx89elR//vOfdeXKFZP9x44d07FjxxQaGqovv/xSjRs3tulrKK3yvEck6erVq3riiSd04cIFY9+JEydMPv/k5GRNmTJFu3fvNjk3IyND0dHRio6O1hdffKE33nhDQ4YMsXidbdu2aebMmUpLSzM7lpiYqMTERO3atUurVq3SihUr5O/vX4pXWbT8EHnr1i19//33Gjp0aJFlt2zZIkmqWrWq+vXrV6br+vr6qmfPnvr+++914sQJHTt2TC1btrRY9tatW/ruu+8kST169CBAAnBIZDbrkNny3GuZjbxWNuQ1ALAN8pp1yGt57rW8JpHZyorMBgC2QWazDpktD5mNzFZaZDYA1mBynROKjo42tjt06FDsoF8aO3bs0KRJk5SVlaUKFSpo8ODB6tevn2rVqqWEhARt3rxZ27dvV0JCgsaMGaO1a9cW+QW/Zs0aJSYmqkGDBnr66afVpk0bpaen67vvvtO6deuUm5ur5cuX6/e//70xwM+ZM0dpaWmKjIzUokWLjH1t27aVJN1///1Wv5b4+HiFhoaqdevWmjBhgmrUqKE9e/booYceMiv74osv6urVqxo0aJAGDhyoKlWqaN++ffroo4+UmZmpdevW6fLly9qxY4fatGmjp556Sk2aNFFCQoI+/fRTHT16VBcvXtT777+vuXPnmtQdFhZm7PPz89OoUaPUunVreXt76/Lly9q/f79CQ0N169YtHTx4UEuXLjV+275Xr17atGlTke+HNTIyMjR+/HhjVnyDBg301FNPqXXr1srJydHBgwe1YsUKXb16VatWrdL169eN2fSF5eTk6M9//rOSkpI0dOhQPfroo6pRo4ZOnjypTz/9VGfOnNHly5f12muv6csvv7S6jeWhvO6RfF999ZWys7P15JNPasCAAbpx44Z27dplPC1069YtPfXUUzp27Jgk6ZFHHlFQUJAaNGigtLQ0HThwQKtWrdK1a9f0yiuvyN3dXYMGDTK5xoEDB/TSSy8pNzdX1apV05NPPqmOHTuqRo0aSkpK0i+//KJVq1YpJSVFp0+f1jvvvKP33ntPktS2bVtt2rRJR48e1auvvipJmjhxovHURElPhQwcOFDz5s1TRkaGwsLCigyR8fHxOnjwoCSpf//+Jk+JWJJ/j8+cOVPR0dHy8/PTp59+Kkny9vaWJIWEhOj777+XJG3evLnI75gffvhBN2/eNM4BAEdEZisZmS3PvZjZyGvkNQBwBOS1kpHX8tyLeU0is5HZAMAxkNlKRmbLQ2Yjs5HZAJQXJtc5oYJPEtSrV88mdaampmrGjBnKysqSh4eHPvzwQz3yyCPG8YCAAD322GNavXq1Zs+erfT0dL300kvaunWrKlSoYFZfYmKi2rdvr+XLl5s8cdCtWzfVrVtX77//vqS8pXvzQ2SjRo0kSf/73/+M8g0bNixyWd7iZGdnq3bt2lq5cqWqVasmSerUqZPFslevXtX06dP19NNPG/s6d+4sDw8PLViwQFJewO7Ro4c++ugjVaxY0XhPfv/73+uxxx4znrIoLP91VqtWTatXrzZeY76+ffvqscce08iRI5WTk6N//vOfRoj08fGRj49Pmd6PL774wgiQ3bp105IlS3TfffeZvM6QkBCNHTtWJ06cUFhYmHr27KnHH3/crK7c3FwlJSXp/fffV//+/Y397dq1U58+ffT444/rypUr2rdvn86fP68GDRpY3U5bK497pKD8AJm/7K+UF/rzLVy40AiQs2fP1h//+EeT87t27ao//vGPGjVqlM6cOaPXX39dPXr0kI+Pj1Hmgw8+UG5urtzd3bVs2TK1b9/epI5evXpp6NChGjRokNLT0/XDDz8oMzNTFStWVJUqVdSqVSulpKQY5evWrWt13/Hx8VHPnj0VHh6uPXv26PLlyxaD55YtW5T/y+IlPdUi/XqP538neHp6mrWpR48eql27ti5fvqywsDBNnTpV7u7uZnVt2rRJklSjRg2T7yoAcCRktpKR2fLci5mNvEZeAwBHQF4rGXktz72Y1yQyG5kNABwDma1kZLY8ZDYyWz4yGwBbMx/94fBSU1ON7Ro1atikzo0bNyopKUmSNG7cuCK/lEeOHKkBAwZIkmJiYiwGp3wzZsywuJTvH/7wB2P7xIkTZWh18QYPHmwEyOI0btzY4u+wFwxKUt7vmOcHyHz33Xef8dRHcnKyybLR8fHxqlKliqpWrarg4GCzAJmvY8eOql+/viSZLKdbVjk5OVq+fLmkvKVp33vvPZMAma9WrVp6//33jX8GPvnkkyLr7N69u9n7IuWFjkcffdT4O3/pYXspj3uksFGjRlncf+PGDa1du1aS1LNnT7MAmc/Pz08zZ86UlNfer7/+2jiWnp6u9PR0eXt7q2fPnmYBMl+DBg2MY7dv39bVq1fv8NWYy3/qIScnR9u2bbNYJiwsTFLePRQYGGiT67q7uxvLQScmJioqKsqszKVLl4xlpYOCgszuSwBwFGQ265DZ7s3MRl4rO/IaAJQdec065LV7M69JZDZbILMBQNmR2axDZiOzSWS2O0VmA1ASJtc5oYKzlTMyMmxS548//mhsP/nkk8WWHTlypMXzCqpevboCAgIsHvP19VXVqlUlyeJvpttKUQNvYV27drX4lEmdOnWM7bp166pJkyYWz69Zs6axXTC81KtXT2FhYfrpp580ffr0Ytvg5+cnScrMzFROTo5V7S7J0aNHjVDRv39/+fr6Fln2gQce0IMPPihJOnbsmC5dumSxXPfu3Yuso2HDhsZ2eX6u1iiPe6Sg6tWrq2nTphaP7d2713j9lpbbLqhr166qVKmSJJmEpfvuu08bNmzQvn37jKWvi1KrVi1j25avtXv37ka/3LJli9nxo0ePKiYmRpKM0GcrBZcz3rx5s9nxzZs3G/dJUUszA4AjILNZh8x2b2Y28lrZkdcAoOzIa9Yhr92beU0is9kCmQ0Ayo7MZh0yG5lNIrPdKTIbgJLws7BOKP83uCXp2rVrNqnz5MmTkqT7779f/v7+xZYNCAiQu7u7srOzi3zCol69enJzcyuyjsqVK+vmzZvKysq680aXoGAILE7+0xGFeXp6Gtv5g2lJ5fKXgi0sP6SmpqYqLi5O58+f15kzZ3T8+HEdPHhQcXFxRtmcnByLoba0Cn421gTqDh06GEHm5MmTFvtBUe+VlPeZ5svOzi5FS22vPO6RgurUqVNk//7vf/9rbM+ZM0dz5syxqs7z589b3J/fF27fvq24uDjFxcUpNjZWJ06c0KFDh4wgJ8lm/4BIeUF88ODBWrZsmaKjo3X69GmT4JwfLCtUqGDzEJn/xMdPP/2kiIgIpaammjztlb/0cZs2bdSyZUubXhsAbInMZh0y272Z2chrZUdeA4CyI69Zh7x2b+Y1icxmC2Q2ACg7Mpt1yGxkNonMdqfIbABKwuQ6J9S4cWNj++LFizapM3+gLfi0QVE8PT1VvXp1Xbt2rcgB2tIyuwUVFzBtJf8pEFuUs/Tb5tY6e/asVqxYoZ07d5r85n1BFSpUsGkAkEzDU8FZ/EUpWKbgMs4FFQyKhRX8TIsK03dLedwjBVla1jvfnYbW69evm+27fPmyvvjiC0VEROjs2bMW39fy6Dv5hg4dqmXLlknKW+p48uTJkvL+SchfErlr1666//77bX7tkJAQ/fTTT0pLS9MPP/xgBNUjR44YwZmnMwA4OjKbdchs92ZmI6/ZBnkNAMqGvGYd8tq9mdckMputkNkAoGzIbNYhs5HZJDJbWZDZABSHyXVOqG3btvLw8FBWVpYOHTqkjIwMk6cESnLx4kUtWbJEXbp0UefOneXv71/qQT+/vC2eJCgv1gZVD4/yuw22bNmiGTNmKDMz09hXtWpVNW3aVA888IDatm2rBx98UG+88Yb27dtn02uX9jMtGEQc+XO1RnncIwUV17cKPp3yzjvvqHnz5lZds/B7vnv3bk2cOFE3b9409t13331q2rSpmjRporZt26pz58768ssvjScWbK1Zs2Zq166dDh8+rK1btxohcvfu3UpMTJQkBQcHl8u1+/fvrzlz5igtLU2bN282QuQ333wjKe+f2YEDB5bLtQHAVshs1iGz3ZuZjbxmG+Q1ACgb8pp1yGv3Zl6TyGy2QmYDgLIhs1mHzEZmI7OVDZkNQHGYXOeEqlSpoi5dumj37t26deuW9u/fX+JvmBcUGRmp0NBQhYaGqm/fvlq8eLG8vb11+fJlJSUllXj+7du3dePGDUmSj4/Pnb4Ml3fq1CkjQFauXFkvvPCC+vbtq4YNG5qFkNTUVJtfv+Bnc+XKlRLLFyzj7J9redwj1iq49LKnp6datWpVqrZLUlJSkl588UXdvHlT7u7ueu655/T444/rgQceMAub6enppa6/NIKDg3X48GGdO3dOhw8fVrt27RQWFiYp7x+ifv36lct1K1eurP79+2vDhg3as2ePrl69Kh8fH3333XeSpF69ejl9PwXg+shszoHMZh/kNdshrwHAnSOvOQfymv2Q2WyHzAYAd47M5hzIbPZDZrMdMhuAojj3NOx7WEhIiLG9cuVKq8/Lzs7WV199ZfwdFBQkSWrRooUk6cKFC7p06VKxdRw5csSYhV7wt8Zhau3atcaTGa+++qrGjRunRo0amQXI3NzcclmiN/8zlaRDhw6VWP7nn382tl3hc7X1PWKtgk9k7Nmzp9iyGRkZWrRokdavX2/y/m/ZssVYDvn555/XlClT1Lx5c4tPziQkJJSqfaU1cOBAVapUSZL0/fff69atW4qIiJCU9xSFl5dXuV07/zPMyclRZGSkfvrpJ129elUSSx8DcB5kNsdHZrMf8pptkNcAoGzIa46PvGZfZDbbILMBQNmQ2Rwfmc2+yGy2QWYDUBQm1zmp/v37q1mzZpKkHTt2aMOGDVadt3TpUp06dUpS3mDXp08fSVKPHj2MMgUHUEsKHi/NrPd7TWxsrLH929/+tshyu3btMnkypuDyudKdL0Xcpk0b+fr6SpL++c9/Fvub96dOnTKWX27WrJnq1KlzR9d0JLa+R6z1u9/9ThUrVpQkhYWFGaHHkg0bNmjx4sV69dVXtX79emO/tX0nJiZG0dHRxt9ZWVkmx22xjHW1atWM9yAiIkI7d+40nii606WPrV2aPDAwUE2aNDGunR9ea9eure7du9/RtQHgbiOzOT4ym/2Q18hrAOAIyGuOj7xmX2Q2MhsAOAIym+Mjs9kXmY3MBqB8MbnOSbm7u2vu3Lny8Mj7Zd/XXntNy5cvNwsg+XJycvTxxx9r4cKFkqSKFStqzpw5xiATEhJiLCX62Wef6ccff7RYT2hoqLZt2yZJaty4sfr27WvLl+VSatSoYWzv2LHDYpljx45pxowZJvsyMjJM/vb09DS209LSrL6+h4eHxowZI0m6efOmpk6dqlu3bpmVu3r1ql566SXl5uZKkp577jmrr+HIbH2PWKtWrVoaNGiQpLxlrV988UWLn9vJkye1YMECSXmhKv+zkkz7zr/+9S+L14mLi9PkyZOVk5Nj7Mt/IijfnfadwvKfhjhz5ow++eQTSXn3f2Bg4B3Vl98ua5b9zr/27t27FR4eLkkaMmSI3N3d7+jaAHC3kdkcH5nNfshr5DUAcATkNcdHXrMvMhuZDQAcAZnN8ZHZ7IvMRmYDUL487N0A3Ll27dpp/vz5+utf/6qsrCzNmzdPa9as0eDBg9W+fXt5e3vr5s2b+uWXX7Rp0ybFxMRIyhsc582bp3bt2hl1Va1aVXPnztWECROUmZmp559/XoMHD1a/fv1Us2ZNXbhwQVu2bDFmSFeqVEkLFy40ZqLD3IABA4zfYF+4cKEuXbqkhx9+WD4+Prp48aJ27NihrVu3moXGGzduqFq1asbffn5+xvby5cvl7e2tnJwcdezYscSAM27cOO3cuVM///yzdu3apaCgII0ZM0atW7dWTk6ODh48qC+++EJXrlyRJD3++ON3POveEdnyHimNadOmaf/+/Tp//rz27t2rQYMG6emnn1abNm1069Yt7d+/XytXrtTNmzclSc8++6xatmxpnN+/f3998sknysnJUWhoqNLT09W/f3/VrFlTV65c0e7du7Vp0ybj/Hw3btww+btg31m7dq2aN28uDw8PtWzZUpUrV7b69XTr1k116tTRxYsXdfToUUl5Qe5O5bcrOTlZS5cuVbdu3eTl5aXf/OY3ZmWHDBmiDz74QBkZGbpw4YKkO38yBADshczm2Mhs9kVeI68BgCMgrzk28pr9kdnIbADgCMhsjo3MZn9kNjIbgPLD5DonN2DAAPn7++u1115TTEyMzp07p0WLFhVZvnHjxvr73/+uTp06mR3r1auXFi9erGnTpunGjRvauHGjNm7caLGOBQsWmAx6MNerVy898cQTWrNmjbKysrRq1SqtWrXKrFyXLl3Utm1bLV++XFLezP26desax3/7298aA/i+ffv0xBNPSJLCw8PVuHHjYttQsWJFLVu2TFOnTtW//vUvnT17Vm+++aZZOTc3Nz3zzDOaMmVKGV6xY7LlPWItb29vrVq1ShMnTtSRI0cUFxenOXPmmJVzc3PTU089palTp5rsb9mypaZMmaL33ntPkrRlyxZt2bLF7PwWLVpowIABev/99yXl9Z3OnTsbx+vWras2bdooOjpaJ0+e1OjRoyXl/TNSmqXLK1SooCFDhujjjz82+ftO9evXz/huWbBggRYsWKAOHTpo7dq1ZmVr166thx9+2HhSpUOHDmratOkdXxsA7IXM5rjIbPZHXiO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      "text/plain": [
       "<Figure size 1452.38x400 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 427,
       "width": 1259
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df.groupby(by=['ID', 'Day', 'Group'])[['Originality', 'Interestingness', 'Writing', 'Coherence']].mean().reset_index()\n",
    "tmp = tmp.rename(columns={'Writing': 'Writing Quality'})\n",
    "tmp2 = pd.melt(tmp, id_vars=[\"ID\", 'Group', 'Day'], var_name='Total', value_name=\"Time\")\n",
    "tmp2.Day = tmp2.Day.astype('str')\n",
    "tmp2.Day.replace({'1':'Writing without AI', '2':'Writing with AI'}, inplace=True)\n",
    "\n",
    "# Set figure size\n",
    "#mpl.rcParams['figure.figsize'] = (2, 3)\n",
    "\n",
    "# Create the catplot\n",
    "ax = sns.catplot(\n",
    "    kind='bar',\n",
    "    data=tmp2,\n",
    "    x='Group',\n",
    "    y='Time',\n",
    "    hue='Day',\n",
    "    col='Total',\n",
    "    order=['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "    palette=sns.color_palette(['gray', 'white']),\n",
    "    edgecolor=\"k\",\n",
    "    width=0.8,\n",
    "    height=4,\n",
    "    aspect=0.8,\n",
    "    errorbar=('ci', 95),\n",
    "    capsize=0.1,\n",
    "    errwidth=0.5\n",
    ")\n",
    "\n",
    "# Remove axis labels\n",
    "ax.set(xlabel='', ylabel='')\n",
    "\n",
    "# Set custom x-tick labels\n",
    "plt.xticks([0, 1, 2], ['Human\\nConfirmation', 'Human\\nCreativity', 'Copilot'])\n",
    "\n",
    "# Move the legend, remove its title, and arrange items side by side\n",
    "sns.move_legend(\n",
    "    ax,\n",
    "    \"upper center\",\n",
    "    bbox_to_anchor=(0.47, 1.1),\n",
    "    fontsize=15,\n",
    "    ncol=2  # Arrange legend items side by side\n",
    ")\n",
    "\n",
    "# Remove the legend title\n",
    "ax._legend.set_title('')\n",
    "\n",
    "# Set the titles of the facets\n",
    "ax.set_titles(\"{col_name}\", size=20)\n",
    "\n",
    "# Annotate bars and adjust margins\n",
    "for axis in ax.axes.ravel():\n",
    "    # Add annotations\n",
    "    for c in axis.containers:\n",
    "        axis.bar_label(c, label_type='edge', fmt='{:,.1f}', padding=10)\n",
    "    axis.margins(y=0.2)\n",
    "\n",
    "# Optionally set y-axis limits\n",
    "plt.ylim(4, 6)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "ax.figure.savefig(\"Fig 2C - Additional Quality Metrics Among Groups.pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca15dbbf",
   "metadata": {},
   "source": [
    "# D. AI Support Removes DAT Inequality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed008be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 384,
       "width": 584
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'df' is your DataFrame with the necessary columns\n",
    "\n",
    "# 1. Data Aggregation with DAT binned in increments of 5\n",
    "min_DAT = df['DAT'].min()\n",
    "max_DAT = df['DAT'].max()\n",
    "\n",
    "# Create bins of width 5 that cover the range of DAT\n",
    "bins = np.arange(np.floor(min_DAT / 2.5) * 2.5, np.ceil(max_DAT / 2.5) * 2.5 + 2.5, 2.5)\n",
    "labels = bins[:-1] + 1.25  # Midpoint of each bin\n",
    "df['DAT_binned'] = pd.cut(df['DAT'], bins=bins, labels=labels, include_lowest=True)\n",
    "df['DAT_binned'] = df['DAT_binned'].astype(float)\n",
    "\n",
    "# Aggregate data using 'DAT_binned'\n",
    "aggregated_data = df.groupby(['DAT_binned', 'Day']).agg(\n",
    "    mean_overall=('Overall', 'mean'),\n",
    "    count=('Overall', 'size')\n",
    ").reset_index()\n",
    "\n",
    "# 2. Conduct Regression Analysis and Prepare for Plotting\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# Add background color shading to three regions\n",
    "ax.axvspan(50, 74, facecolor='gray', alpha=0.2)  # Left region (x < 74)\n",
    "ax.axvspan(74, 82, facecolor='lightgray', alpha=0.2)  # Middle region (74 <= x <= 82)\n",
    "ax.axvspan(82, 100, facecolor='white', alpha=0.2)  # Right region (82 < x)\n",
    "\n",
    "# Remove grid\n",
    "ax.grid(False)\n",
    "\n",
    "# Define markers and line styles for each Day\n",
    "day_markers = {\n",
    "    1: 'o',  # Circle\n",
    "    2: 'x',  # X marker instead of square\n",
    "}\n",
    "\n",
    "day_linestyles = {\n",
    "    1: 'solid',\n",
    "    2: 'dashed',\n",
    "}\n",
    "\n",
    "days = sorted(df['Day'].unique())\n",
    "count_min = aggregated_data['count'].min()\n",
    "count_max = aggregated_data['count'].max()\n",
    "\n",
    "def map_size(count, count_min, count_max, size_min=40, size_max=200):\n",
    "    if count_max == count_min:\n",
    "        return size_min\n",
    "    else:\n",
    "        return size_min + (count - count_min) * (size_max - size_min) / (count_max - count_min)\n",
    "\n",
    "legend_handles = []\n",
    "\n",
    "for day in days:\n",
    "    day_data_agg = aggregated_data[aggregated_data['Day'] == day]\n",
    "    x_agg = day_data_agg['DAT_binned']\n",
    "    y_agg = day_data_agg['mean_overall']\n",
    "    counts = day_data_agg['count']\n",
    "    sizes = counts.apply(lambda c: map_size(c, count_min, count_max))\n",
    "    marker = day_markers.get(day, 'o')\n",
    "    \n",
    "    # Set facecolors only for markers that have a fill (circles, squares), not 'x'\n",
    "    if marker in ['o', 's']:  # Circle, Square\n",
    "        facecolor = 'none'\n",
    "    else:\n",
    "        facecolor = 'black'  # Set default color for 'x'\n",
    "\n",
    "    ax.scatter(\n",
    "        x_agg, y_agg, s=sizes, marker=marker, facecolors=facecolor,\n",
    "        edgecolors='black', alpha=0.5, zorder=2\n",
    "    )\n",
    "    \n",
    "    df[['DAT', 'Overall', 'Experience', 'Ability', 'English']] = df[['DAT', 'Overall', 'Experience', 'Ability', 'English']].apply(pd.to_numeric, errors='coerce')\n",
    "    \n",
    "    day_data = df[df['Day'] == day].dropna(subset=['DAT', 'Overall', 'Experience', 'Ability', 'English'])\n",
    "    \n",
    "    if len(day_data) < 2:\n",
    "        print(f\"Not enough data points for Day '{day}' to perform regression.\")\n",
    "        continue\n",
    "    \n",
    "    y = day_data['Overall']\n",
    "    X = day_data[['DAT', 'Experience', 'Ability', 'English']]\n",
    "    X = sm.add_constant(X)\n",
    "    \n",
    "    model = sm.OLS(y, X).fit()\n",
    "    \n",
    "    coef_DAT = model.params['DAT']\n",
    "    ci_lower_DAT, ci_upper_DAT = model.conf_int().loc['DAT']\n",
    "    \n",
    "    DAT_range = np.linspace(day_data['DAT'].min(), 100, 100)\n",
    "    mean_Experience = day_data['Experience'].mean()\n",
    "    mean_Ability = day_data['Ability'].mean()\n",
    "    mean_English = day_data['English'].mean()\n",
    "    \n",
    "    X_pred = pd.DataFrame({\n",
    "        'const': 1,\n",
    "        'DAT': DAT_range,\n",
    "        'Experience': mean_Experience,\n",
    "        'Ability': mean_Ability,\n",
    "        'English': mean_English\n",
    "    })\n",
    "    \n",
    "    y_pred = model.predict(X_pred)\n",
    "    \n",
    "    linestyle = day_linestyles.get(day, 'solid')\n",
    "    \n",
    "    ax.plot(\n",
    "        DAT_range, y_pred, linestyle=linestyle, color='black', zorder=3\n",
    "    )\n",
    "    \n",
    "    slope_formatted = f\"{coef_DAT:.3f}\"\n",
    "    ci_lower_formatted = f\"{ci_lower_DAT:.3f}\"\n",
    "    ci_upper_formatted = f\"{ci_upper_DAT:.3f}\"\n",
    "    label = f\"Day {day} (Slope: {slope_formatted}, 95% CI = [{ci_lower_formatted}, {ci_upper_formatted}])\"\n",
    "    \n",
    "    line_handle = Line2D(\n",
    "        [0], [0],\n",
    "        color='black',\n",
    "        linestyle=linestyle,\n",
    "        marker=marker,\n",
    "        markerfacecolor='black',\n",
    "        markeredgecolor='black',\n",
    "        markersize=8,\n",
    "        label=label\n",
    "    )\n",
    "    legend_handles.append(line_handle)\n",
    "    \n",
    "ax.set_xlim(50, 100)\n",
    "ax.set_ylim(3, 6.5)\n",
    "\n",
    "ax.legend(handles=legend_handles, frameon=False, loc='lower left')\n",
    "\n",
    "ax.set_xlabel('DAT', labelpad=5, fontsize=14)\n",
    "ax.set_ylabel('Overall Quality', labelpad=5, fontsize=14)\n",
    "\n",
    "ax.text(62, 5.8, 'Low\\nDAT\\n< 74', horizontalalignment='center', fontsize=13)\n",
    "ax.text(78, 5.8, 'Average\\nDAT\\n[74,82]', horizontalalignment='center', fontsize=13)\n",
    "ax.text(91, 5.8, 'High\\nDAT\\n> 82', horizontalalignment='center', fontsize=13)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "ax.figure.savefig(\"Fig 2D - AI Support Removes DAT Inequality.pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fcb47e",
   "metadata": {},
   "source": [
    "# E. AI Effect Varies Among DAT Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a2f2c5e",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1020.25x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 541,
       "width": 857
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Subset the data for Day == 2\n",
    "data = df[df.Day == 2]\n",
    "\n",
    "# Fit the linear regression model controlling for English, Ability, Experience\n",
    "model = smf.ols('Overall ~ C(DAT_Group) * C(Group) + English + Ability + Experience', data=data).fit()\n",
    "\n",
    "# Create a new DataFrame with all combinations of DAT_Group and Group\n",
    "DAT_Groups = data['DAT_Group'].unique()\n",
    "Groups = data['Group'].unique()\n",
    "\n",
    "mean_English = data['English'].mean()\n",
    "mean_Ability = data['Ability'].mean()\n",
    "mean_Experience = data['Experience'].mean()\n",
    "\n",
    "predict_df = pd.DataFrame(list(itertools.product(DAT_Groups, Groups)), columns=['DAT_Group', 'Group'])\n",
    "predict_df['English'] = mean_English\n",
    "predict_df['Ability'] = mean_Ability\n",
    "predict_df['Experience'] = mean_Experience\n",
    "\n",
    "# Get predictions and confidence intervals\n",
    "predictions = model.get_prediction(predict_df)\n",
    "pred_summary = predictions.summary_frame(alpha=0.05)  # 95% CI\n",
    "\n",
    "# Combine predictions with predict_df\n",
    "result_df = pd.concat([predict_df.reset_index(drop=True), pred_summary.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# Calculate error bars\n",
    "result_df['error_lower'] = result_df['mean'] - result_df['mean_ci_lower']\n",
    "result_df['error_upper'] = result_df['mean_ci_upper'] - result_df['mean']\n",
    "\n",
    "# Map 'DAT_Group' to labels\n",
    "DAT_Group_order = ['Low\\n(DAT < 74)', 'Average\\n(74 <= DAT <= 82)', 'High\\n(82 < DAT)']\n",
    "DAT_Group_mapping = dict(zip(sorted(DAT_Groups), DAT_Group_order))\n",
    "result_df['DAT_Group_Label'] = result_df['DAT_Group'].map(DAT_Group_mapping)\n",
    "\n",
    "# Sort the DataFrame for consistent plotting\n",
    "result_df['DAT_Group_Label'] = pd.Categorical(result_df['DAT_Group_Label'], categories=DAT_Group_order, ordered=True)\n",
    "result_df['Group'] = pd.Categorical(result_df['Group'], categories=['Human Confirmation', 'Human Creativity', 'Copilot'], ordered=True)\n",
    "result_df = result_df.sort_values(by=['DAT_Group_Label', 'Group'])\n",
    "\n",
    "# Create the catplot\n",
    "#sns.set_style('whitegrid')\n",
    "\n",
    "# Set figure size using height and aspect\n",
    "ax = sns.catplot(\n",
    "    kind='bar',\n",
    "    data=result_df,\n",
    "    x='DAT_Group_Label',\n",
    "    y='mean',\n",
    "    hue='Group',\n",
    "    hue_order=['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "    palette=sns.color_palette(['darkgray', 'lightgray', 'white']),\n",
    "    edgecolor=\"k\",\n",
    "    width=0.75,\n",
    "    height=5.5,\n",
    "    aspect=1.5,  # height=5 and aspect=6/5 to set the figure size to (6,5)\n",
    "    ci=None,\n",
    "    capsize=0.1,\n",
    "    errwidth=0.5\n",
    ")\n",
    "\n",
    "# Add custom error bars\n",
    "ax.axes[0, 0].errorbar(\n",
    "    x=np.arange(len(result_df['DAT_Group_Label'].cat.categories)).repeat(3) + np.tile([-0.25, 0, 0.25], len(result_df['DAT_Group_Label'].cat.categories)),\n",
    "    y=result_df['mean'],\n",
    "    yerr=[result_df['error_lower'], result_df['error_upper']],\n",
    "    fmt='none',\n",
    "    c='k',\n",
    "    capsize=3,\n",
    "    elinewidth=0.5\n",
    ")\n",
    "\n",
    "# Customize labels and titles\n",
    "ax.ax.set(xlabel='', ylabel='')\n",
    "\n",
    "# Set y-axis ticks with increments of 1\n",
    "ax.ax.set(ylim=(4, 6))  # Set y-axis limits between 4 and 6\n",
    "#ax.ax.set_yticks(np.arange(4, 7, 1))  # Set yticks with increments of 1\n",
    "\n",
    "# Set the font size for the y-axis tick labels\n",
    "plt.setp(ax.ax.get_yticklabels(), fontsize=16) \n",
    "\n",
    "ax.ax.set_ylabel('Overall Quality (Day 2, With AI)', fontsize=20, labelpad=10)  # Corrected ylabel\n",
    "ax.ax.set_xticklabels(DAT_Group_order, fontsize=18)\n",
    "#ax.ax.set_title(\"{col_name}\", size=15)\n",
    "\n",
    "# Move and customize the legend\n",
    "sns.move_legend(\n",
    "    ax,\n",
    "    \"upper center\",\n",
    "    bbox_to_anchor=(0.43, 1),\n",
    "    fontsize=18,\n",
    "    ncol=3  # Arrange legend items side by side\n",
    ")\n",
    "\n",
    "# Remove the legend title and set custom labels for the legend\n",
    "ax._legend.set_title(None)\n",
    "new_labels = ['Human\\nConfirmation', 'Human\\nCreativity', 'Copilot']\n",
    "for t, l in zip(ax._legend.texts, new_labels):\n",
    "    t.set_text(l)\n",
    "\n",
    "# Add annotations (bar labels)\n",
    "for container in ax.ax.containers:\n",
    "    if hasattr(container, 'datavalues'):  # Only apply to bar containers\n",
    "        labels = [f'{v:.1f}' for v in container.datavalues]\n",
    "        ax.ax.bar_label(container, labels=labels, label_type='edge', padding=10, fontsize=18)\n",
    "\n",
    "# Ensure proper margins and display\n",
    "ax.ax.margins(y=0.2)\n",
    "plt.show()\n",
    "\n",
    "ax.figure.savefig(\"Fig 2E - AI Effect Varies Among DAT Groups.pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbc42dab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
